TY - JOUR
T1 - Real-time Zika risk assessment in the United States
JF - bioRxiv
DO - 10.1101/056648
SP - 056648
AU - Castro, Lauren A
AU - Fox, Spencer J
AU - Chen, Xi
AU - Liu, Kai
AU - Bellan, Steve
AU - Dimitrov, Nedialko B
AU - Galvani, Alison P
AU - Meyers, Lauren Ancel
Y1 - 2016/01/01
UR - http://biorxiv.org/content/early/2016/06/07/056648.abstract
N2 - Background: The southern United States (US) may be vulnerable to outbreaks of Zika Virus (ZIKV), given its broad distribution of ZIKV vector species and periodic ZIKV introductions by travelers returning from affected regions. As cases mount within the US, policymakers seek early and accurate indicators of self-sustaining local transmission to inform intervention efforts. However, given ZIKV's low reporting rates and geographic variability in both importations and transmission potential, a small cluster of reported cases may reflect diverse scenarios, ranging from multiple self-limiting but independent introductions to a self-sustaining local outbreak.Methods and Findings: We developed a stochastic model that captures variation and uncertainty in ZIKV case reporting, importations, and transmission, and applied it to assess county-level risk throughout the state of Texas. For each of the 254 counties, we identified surveillance triggers (i.e., cumulative reported case thresholds) that robustly indicate further epidemic expansion. Regions of greatest risk for sustained ZIKV transmission include 33 Texas counties along the Texas-Mexico border, in the Houston Metro Area, and throughout the I-35 Corridor from San Antonio to Waco. Across this region, variation in reporting rates, ZIKV introductions, and vector habitat suitability drives variation in the recommended surveillance triggers for public health response. For high risk Texas counties, we found that, for a reporting rate of 20%, a trigger of two cumulative reported cases corresponds to a 60% chance of an ongoing local transmission.Conclusions: With reliable estimates of key epidemiological parameters, including reporting rates and vector abundance, this framework can help optimize the timing and spatial allocation of public health resources to fight ZIKV in the US.
ER -